1. Field of the Invention
The present to geophysical exploration, and more particularly to processing of time lapse or 4D seismic data for evaluation of features of interest regarding subsurface formations and their contents.
2. Description of the Related Art
Seismic reflectivity, also known as seismic amplitude or reflection strength, is related to differences in acoustic impedance between reservoir rock and overlying strata. Changing reservoir fluids can modify reservoir acoustic impedance which is calculated by multiplying seismic energy travel velocity and rock density. Seismic reflection strength at a reservoir boundary can vary due to fluid changes such as injection of gas or steam, causing changes in either density, velocity or both. Changes in temperature and pressure also influence the reservoir acoustic impedance. Monitoring movement of these dynamic changes between wells is made possible by conducting repeated time lapse seismic surveys. In this way fluid position is tracked over time by differencing the reflection amplitude between two or more surveys at different times. Other seismic attributes can be differenced but reflection strength is the most commonly used. Further descriptions of these types of surveys and data processing are contained, for example, in “Spectral analysis applied to seismic monitoring of thermal recovery”, SEG Expanded Abstracts 12, 331-334 (1993), Eastwood et al. (1993); “Processing for robust time-lapse seismic analysis: Gulf of Mexico example, Lena Field”, SEG Expanded Abstracts 17, 20-23 (1998), Eastwood et al.; “Time lapse processing: A North Sea case study”, 68th Ann. Internat. Mtg., SEG, Expanded Abstracts, 1-4, Harris et al. (1998); “Schiehallion: A 3-D Time-Lapse Processing Case History, SEG 1999 Expanded Abstracts; Altan et al. (1999); and “4D seismic monitoring of CO2 flood in a thin fractured carbonate reservoir”, The Leading Edge, July 2003, 691-695, Li (2003).
Reservoir characterization based on seismic observations has required a highly accurate seismic acquisition and processing system. For the specialized case of integrating time lapse seismic surveys with reservoir monitoring, accuracy requirements have become even more crucial since dynamic reservoir changes such as fluid movement or pressure changes are related to subtle differences in seismic observations. See, for example, “Time Lapse Seismic Reservoir Monitoring”, Geophysics, Vol. 66, No. 1 (January-February 2001); P. 50-53, Lumley.
Under ideal conditions, differencing two seismic observations has been a straight forward process so long as the resultant value is assumed to only reflect changes at the reservoir level. Since recorded seismic energy propagated through a geologic overburden, and was also subject to the recording response of the acquisition system, two key assumptions have been used to interpret reservoir amplitude change. These were that propagation effects in the geologic overburden remained the same, and that seismic recording systems responded identically during independent monitoring surveys at different times.
Field studies have routinely shown these basic assumptions to be false. One only needs to consider the recording system can never be placed exactly in the same surface position, and that changes in the overburden do in fact occur. For example, near surface seasonal variations such as water table elevation changed the overburden response. Even daily temperature and moisture changes in the first few feet of soil affected repeated seismic observations.
To improve repeatability in recording, systems have been developed that feature permanently cemented sources and detectors, an example of which is described in “Reservoir monitoring using permanent sources and vertical receiver antennae”, The Céré-la-Ronde case study, The Leading Edge, June 2001, 622-629, (Meunier et al.). Unfortunately there still existed possible overburden changes, especially in the near surface, that occurred above and below the level of permanently installed recording systems. An example of seismic energy above a buried system was the effect of reflections from the air/surface interface commonly known as “ghost energy.”
Changes in overburden propagation response are routinely compensated for during 4D seismic data processing. Although there may be several causes, overburden changes are usually treated as a single effect. This has been done by conditioning the data using processing techniques that forced overburden measurements to be the same between surveys, or to be the same with those recorded in an initial survey. This processing method prior to differencing is commonly referred to as “cross equalization of a monitor survey to the base survey.” Once cross equalization of the overburden was applied, corrected reservoir amplitudes were differenced between surveys to observe dynamic changes in the reservoir, such as movement of injected fluids, pressure fronts and temperature fronts. Examples of cross equalization are described in “Inside the cross-equalization black box”, The Leading Edge, 15, 1233-1240, (Ross et al., 1938); “A cross-equalization processing flow for off-the-shelf 4-D seismic data”, 68th Ann. Internat. Mtg., SEG., Expanded Abstracts, 16-19, (Rickett et al., 1998); and “Seismic Low-Frequency Effects in Monitoring Fluid-Saturated Reservoirs”, Geophysics, Vol. 69, No. 2 (March-April 2004); p. 522-532, Korneev et al.
Performed as a frequency dependent process, cross-equalization can be robust if the frequency bandwidth is similar between two surveys. If not, then cross equalized surveys are limited to a common bandwidth between surveys which may lower resolution of the data. Another key assumption is that signal-to-noise ratios at each processed frequency are the same. While this may be the case for certain types of repeatable noise, such as source generated noise, it does not address frequency dependent noise variations between time lapse surveys.
Non-repeatable frequency dependent noise may be caused by seasonal or daily changes in near surface overburden layers. As such, cross-equalization tends to propagate noise present in one survey into other surveys. This effect tends to decrease repeatability outside the cross-equalization design window which makes it more difficult to observe small seismic amplitude changes caused by dynamic fluid, pressure or temperature changes in the reservoir.
Another state-of-the-art 4D data processing normalization technique employs overburden time domain windows to correct the target reservoir. The procedure calculates an average amplitude value from an overburden window that is divided into the average amplitude value of the reservoir window. Since the correction is computed in the time domain, all frequencies contribute to the final correction factor. Similar to cross equalization, deficiencies in this method arise when certain frequency bandwidths are dominated by noise events that vary across time lapse surveys.